The question of whether LLMs are a dead end, as discussed by R. Sutton, Y. LeCun, and T. Ord, among many others, is hundreds of years old. Currently, we see that the chance of an LLM agent failing a task rises with the number of steps taken. This was observed even before the era of LLMs, when agents were trained with imitation learning. The crux is whether further training of LLMs leads to the completion of longer tasks or if these agents hit a wall. Do LLMs indeed build a real-world model that allows them to take the right actions in long time horizon tasks? Yet, models might only build a model from predicting what humans would say as their next token. Then the question is whether humans possess the necessary knowledge.

Questions like "what is knowledge, and do we have it?" are hundreds of years old. Aristotle wrote that the basis for every statement, and thus reasoning and thinking (1005b5), is that "A or not A" (the law of identity). In other words, it is impossible for something to be and not be what it is simultaneously. This is the beginning of all reasoning. This law opposes the view of sophists like Protagoras, who claimed that what we sense or our opinions constitute knowledge. Sophists held that something can both be and not be what it is at the same time, or "everything flows" (panta rhei, Heraclitus). Plato and Aristotle opposed this view. The law of identity suggests that ground truth is essential for correct reasoning and action. And it’s not about mathematical problems where LLMs show impressive results; it’s about reasoning and acting in the real world. So far, LLMs are taught mainly based on predicting what people would say next—opinions rather than real-world experience.

Why are LLMs trained on opinions? Their pre-training corpus is over 99% composed of people’s opinions and not real-world experience. The entire history of knowledge is a struggle to find the truth and overcome falsehoods and fallacies. The artifacts remaining from this struggle are filled with false beliefs. Even our best thinkers were wrong in some sense, like Aristotle, who believed slavery wasn’t a bad thing (see his Politics). We train LLMs not only on the artifacts from our best thinkers but, in 99.95% of cases, on web crawls, social media, and code. The largest bulk of compute is spent on pre-training, not on post-training for real-world tasks. Whether the data is mostly false or can serve as a good foundation for training on real-world tasks remains an open question. Can a model trained to predict opinions without real experience behave correctly? This is what reinforcement learning addresses.

Reinforcement learning involves learning from experience in the search for something good. Plato depicted this beautifully in his allegory of the cave, where a seeker finds truth on the path to the Sun. A real-world model is built from seeking something good. The current standard model of an intelligent agent reflects what Aristotle described about human nature: conscious decisions, behavior, and reasoning to achieve good (Nicomachean Ethics, 1139a30). LLMs are mostly trained on predicting the next token, not achieving something good. Perhaps Moravec's paradox results from this training; models don’t possess the general knowledge or reasoning. General reasoning might be required to build economically impactful agents. General reasoning is the thinking using the real world knowledge in novel situations. Will models learn it someday?